A Video Analytics System for Person Detection Combined with Edge Computing

Maltezos, Evangelos; Lioupis, Panagiotis; Dadoukis, Aris; Karagiannidis, Lazaros; Ouzounoglou, Eleftherios; Krommyda, Maria; Amditis, Angelos

doi:10.3390/computation10030035

Cited by 9 publications

(4 citation statements)

References 65 publications

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“…Among the practical applications of machine learning systems, the following can be distinguished. First, research about recognizing elements in the image may also inspire analyzing single or multi-modal signals such as EOG [48,49], recognition of speech by detection using wearable devices around the head with all interference caused by such devices [50], written text, navigation in an unknown area, recommendation systems, guidance, forecasting financial and economic trends, and more pioneering research can open up new areas [51].…”

Section: Discussionmentioning

confidence: 99%

Classification of Roads and Types of Public Roads Using EOG Smart Glasses and an Algorithm Based on Machine Learning While Driving a Car

Doniec

Piaseczna

et al. 2022

Electronics

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Driving a car is an activity that became necessary for exploration, even when living in the present world. Research exploring the topic of safety on the roads has therefore become increasingly relevant. In this paper, we propose a recognition algorithm based on physiological signals acquired from JINS MEME ES_R smart glasses (electrooculography, acceleration and angular velocity) to classify four commonly encountered road types: city road, highway, housing estate and undeveloped area. Data from 30 drivers were acquired in real driving conditions. Hand-crafted statistical features were extracted from the physiological signals to train and evaluate a random forest classifier. We achieved an overall accuracy, precision, recall and F1 score of 87.64%, 86.30%, 88.12% and 87.08% on the test dataset, respectively.

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Section: Discussionmentioning

confidence: 99%

Classification of Roads and Types of Public Roads Using EOG Smart Glasses and an Algorithm Based on Machine Learning While Driving a Car

Doniec

Piaseczna

et al. 2022

Electronics

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show abstract

“…Furthermore, ref. [43] introduced a real-time method for performing video analytics on an NVIDIA Jeston TX2, an edge device. This approach was augmented with a graphics processing unit (GPU) to facilitate the detection of individuals in restricted areas through the utilization of YOLOv5s [44], which generates a range of object outputs, such as bounding boxes, class names, and probabilities.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection on the Edge Using Smart Cameras under Low-Light Conditions

Abu Awwad,

Rana,

Perera

2024

Sensors

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The number of cameras utilised in smart city domains is increasingly prominent and notable for monitoring outdoor urban and rural areas such as farms and forests to deter thefts of farming machinery and livestock, as well as monitoring workers to guarantee their safety. However, anomaly detection tasks become much more challenging in environments with low-light conditions. Consequently, achieving efficient outcomes in recognising surrounding behaviours and events becomes difficult. Therefore, this research has developed a technique to enhance images captured in poor visibility. This enhancement aims to boost object detection accuracy and mitigate false positive detections. The proposed technique consists of several stages. In the first stage, features are extracted from input images. Subsequently, a classifier assigns a unique label to indicate the optimum model among multi-enhancement networks. In addition, it can distinguish scenes captured with sufficient light from low-light ones. Finally, a detection algorithm is applied to identify objects. Each task was implemented on a separate IoT-edge device, improving detection performance on the ExDark database with a nearly one-second response time across all stages.

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“…Ref. [12] introduced a stable and effective object tracking system called video analytics edge computing (VAEC) by adopting a tracking-by-detection (TBD) method, which provided a real-time increased context awareness for human detection in video surveillance. Ref.…”

Section: Related Workmentioning

confidence: 99%

Cognitive Video Surveillance Management in Hierarchical Edge Computing System with Long Short-Term Memory Model

Ugli

Kim²,

Mohammed

et al. 2023

Sensors

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Nowadays, deep learning (DL)-based video surveillance services are widely used in smart cities because of their ability to accurately identify and track objects, such as vehicles and pedestrians, in real time. This allows a more efficient traffic management and improved public safety. However, DL-based video surveillance services that require object movement and motion tracking (e.g., for detecting abnormal object behaviors) can consume a substantial amount of computing and memory capacity, such as (i) GPU computing resources for model inference and (ii) GPU memory resources for model loading. This paper presents a novel cognitive video surveillance management with long short-term memory (LSTM) model, denoted as the CogVSM framework. We consider DL-based video surveillance services in a hierarchical edge computing system. The proposed CogVSM forecasts object appearance patterns and smooths out the forecast results needed for an adaptive model release. Here, we aim to reduce standby GPU memory by model release while avoiding unnecessary model reloads for a sudden object appearance. CogVSM hinges on an LSTM-based deep learning architecture explicitly designed for future object appearance pattern prediction by training previous time-series patterns to achieve these objectives. By referring to the result of the LSTM-based prediction, the proposed framework controls the threshold time value in a dynamic manner by using an exponential weighted moving average (EWMA) technique. Comparative evaluations on both simulated and real-world measurement data on the commercial edge devices prove that the LSTM-based model in the CogVSM can achieve a high predictive accuracy, i.e., a root-mean-square error metric of 0.795. In addition, the suggested framework utilizes up to 32.1% less GPU memory than the baseline and 8.9% less than previous work.

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A Video Analytics System for Person Detection Combined with Edge Computing

Cited by 9 publications

References 65 publications

Classification of Roads and Types of Public Roads Using EOG Smart Glasses and an Algorithm Based on Machine Learning While Driving a Car

Classification of Roads and Types of Public Roads Using EOG Smart Glasses and an Algorithm Based on Machine Learning While Driving a Car

Anomaly Detection on the Edge Using Smart Cameras under Low-Light Conditions

Cognitive Video Surveillance Management in Hierarchical Edge Computing System with Long Short-Term Memory Model

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